Target Detection and Classification Based on LiDAR

Authors

  • TANG Chun-ming School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
  • ZHANG Xiao-yu School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
  • YU Xiang School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
  • ZHU Wen-yan School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China

Keywords:

, LiDAR, Target detection and classification, RDforest new model, Seed area, Classification credibility.

Abstract

To solve the problem of difficult classification of air baggage, we use LMS511 LiDAR to collect the distance data from the baggage surface to the light-center of LiDAR, propose a new detection and classification algorithm, and the baggage detection and classification system is designed, thus, the self-service of air baggage check is realized. Firstly, an object-based classification method is proposed by considering the characteristics of target. The geometry, texture, corner features and shape descriptors of the baggage are extracted to construct the feature vectors, and the feature vectors are imported into the RDforest new model to classify the baggage samples. Secondly, based on the three-dimensional characteristics of LiDAR data, a classification method based on Seed area is proposed. By comparing the classification credibility values of two classification methods, the further classification results reached 91.33%. In addition, the filling rate and the average Gaussian curvature entropy were used to classify the hard shell packing box and the Luggage case in detail, and the classification results reached 100%. The experimental results show that target detection and classification system is more robust and has better recognition and classification effects.

References

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Published

2018-11-18

How to Cite

Chun-ming, T., Xiao-yu, Z., Xiang, Y., & Wen-yan, Z. (2018). Target Detection and Classification Based on LiDAR. American Scientific Research Journal for Engineering, Technology, and Sciences, 49(1), 28–39. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/4493

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Articles